The more real-time and granular your data is, the more responsive and competitive your organization can become. While the Hadoop MapReduce framework is excellent for collecting and storing big data, real-time operations like stream processing, cloud analytics and sophisticated computations require far more speed. Apache Spark delivers unheard of speed and flexibility to data scientists and developers working with big data at scale. It's built for speed, ease of use, and advanced analytics-and it can run anywhere, including Hadoop. Join IBM experts for a look at Spark and how it can enhance your big data analytics by: Supporting your interactive data mining, cloud and streaming efforts, Combining SQL, streaming and complex analytics in the same application to handle a range of processing scenarios, Complementing your existing analytics investments for the widest variety of data types and analytics workloads